CN108872308B - Detection and research method for ground heating wood floor heat slow-release rule - Google Patents
Detection and research method for ground heating wood floor heat slow-release rule Download PDFInfo
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Abstract
The invention discloses a method for detecting and researching the heat slow-release rule of a ground heating wood floor, which establishes a heat release vacuum detection environment aiming at a heterogeneous material by using the method for detecting and researching the heat slow-release rule of the ground heating wood floor, removes the influence of two heat transfer modes of convection and heat conduction on the heat slow-release characteristic of the ground heating wood floor, ensures that a research target only aims at one heat transfer mode of heat radiation, and has pertinence to a research object; meanwhile, a least square support vector machine is adopted to establish a temperature value prediction model for different space positions and different time in the closed cavity, and a space point temperature value which cannot be directly measured and a curve of the space point temperature value which changes along with time are predicted, so that the time and material cost of experimental research are reduced, the credibility of a research result of the heat release characteristic of the ground heating wood floor is increased, and a scientific experiment and analysis tool is provided for the subsequent heat release rule of the ground heating wood floor which is further dug deeply.
Description
Technical Field
The invention relates to a method for detecting and researching a heat slow-release rule of a ground heating wood floor, and belongs to the field of research of the heat slow-release rule of the ground heating wood floor.
Background
In office and home environments, a wood floor is a common ground heating floor material, when the wood floor is heated, stored heat can be slowly released, and the wood floor is a heterogeneous material, the internal structure of the wood floor is influenced by cell composition, arrangement mode and growth cycle, so that the wood floor has the characteristic of anisotropy, and a new detection device and a detection method must be researched aiming at the heat slow release rule of the ground heating floor when the mechanism and the characteristic of slow release of the heat are researched, and a research method for homogeneous materials cannot be simply used. Therefore, a detection and research method aiming at the heat slow release rule of the ground heating wood floor is urgently needed.
Disclosure of Invention
The invention provides a device and a method for researching the heat slow-release rule of a heterogeneous material mainly based on a ground heating wood floor, aiming at solving the problems in the prior art, and aims to solve the limitation of the traditional detection method on the detection of the heat slow-release characteristic of the heterogeneous material.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the invention provides a method for detecting and researching a heat slow-release rule of a ground heating wood floor, which comprises the following steps:
(1) the thermal slow release detection device is arranged to be a cylindrical closed cavity, the upper surface and the lower surface of the closed cavity are of cambered surface structures, the upper surface is of an open-close type design, the upper cavity is opened, a wood floor sample to be tested is placed on a tray at the bottom of the closed cavity, the cavity walls are double-layer, vacuum environments are arranged between the cavity walls and in the closed cavity, only one heat transfer mode of radiation is guaranteed to be in the closed cavity, and meanwhile, the closed cavity is guaranteed to be insulated from the outside;
(2) the bottom of the closed cavity is provided with a tray for placing the wooden floor sample, and meanwhile, the wooden floor sample is heated through a heating cavity below the tray and is kept in close contact with the heating cavity, so that the heat in the heating cavity is conducted to the closed cavity in a radiation mode only through the wooden floor sample above the tray;
(3) a U-shaped water pipe is arranged in the heating cavity, the water pipe is connected into a heating water tank on the outer side, and the water temperature in the heating cavity is controlled through the heating water tank, so that the temperature in the heating cavity can be constantly kept in a set temperature range;
(4) during the experiment, firstly, a wooden floor sample is placed on a tray in a closed cavity through an upper cavity, then the closed cavity is closed, a vacuum pump is used for pumping the closed cavity into a required vacuum environment, the vacuum degree in the cavity is detected through a vacuum meter on the outer wall of the closed cavity, and when the required test vacuum condition is achieved, the wooden floor sample on the tray is heated through a heating cavity;
(5) a temperature sensor array is arranged in the closed cavity, the wood floor sample starts to release heat to the closed cavity in a radiation mode after being heated, temperature values of sensors at different positions in the closed cavity at different moments are collected through the temperature sensor array and stored in an upper computer;
(6) the temperature in the heating water tank is controlled by PID, meanwhile, the heating water tank and warm water in the heating cavity are kept in a flowing state to ensure that the temperature in the heating cavity is constant, and when the temperature in the closed cavity reaches a balance state, the test is finished;
(7) establishing a least square support vector machine prediction model and training by using temperature values obtained on space points corresponding to positions of each sensor, wherein the training is machine learning training, the least square support vector machine model is trained through obtained experimental data, input variables are space coordinates and time of temperature values of sampling points, output variables are temperature values of the sampling points, the input variables and the output variables are respectively brought into the least square support vector machine model for machine learning, training parameters are adjusted to obtain the least square support vector machine prediction model with the best training effect, the model can be used for predicting the temperature values at any space positions, and a curve of the temperature values changing along with time is obtained;
(8) inserting a plurality of prediction points between adjacent sensors in a closed cavity to form an increment space point, and predicting a temperature value on the increment space point by using a trained least square support vector machine model;
(9) combining the predicted temperature values on the incremental space points with the temperature values acquired in the test to form a new training sample, retraining the least square support vector machine model by using the sample to generate a new prediction model, and predicting the temperature values on the next group of incremental space points to obtain the temperature values on any space points;
(10) the temperature values of a plurality of times in the temperature balancing process in the closed cavity are trained, and the change curves of the temperature values at different space positions along with the time can be obtained.
Therefore, by using the detection and research method aiming at the heat slow release rule of the ground heating wood floor, the heat release vacuum detection environment aiming at the heterogeneous material is established, the influence of two heat transfer modes of convection and heat conduction on the slow release characteristic of the ground heating wood floor can be removed, the research target is only aimed at one heat transfer mode of heat radiation, and the research object is more targeted; meanwhile, a least square support vector machine is adopted to establish a temperature value prediction model for different space positions and different time in the closed cavity, the temperature value of a space point which cannot be directly measured and a curve of the temperature value changing along with the time can be predicted, the time and material cost of experimental research is reduced, the credibility of the research result of the heat release characteristic of the ground heating wood floor is increased, and scientific experimental and analytical tools can be provided for the subsequent heat release rule of the ground heating wood floor further dug deeply.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic structural diagram of a method for detecting and researching a heat slow-release law of a ground heating wood floor according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below, and the embodiments described by referring to the drawings are exemplary only for the purpose of illustrating the present invention and are not to be construed as limiting the present invention. This is further explained below with reference to the drawings.
As shown in fig. 1, the method for detecting and researching the heat slow-release law of the ground heating wood floor comprises the following steps:
(1) the thermal slow release detection device is a cylindrical closed cavity, the upper surface and the lower surface of the cavity are of arc-surface structures, the upper surface is of an open-close type design, the experimental wooden floor sample can be placed on a tray at the bottom of the closed cavity in a mode of opening the upper cavity, the cavity wall is of a double-layer structure design, vacuum environments are formed between the cavity walls and in the closed cavity, only one heat transfer mode of radiation can be guaranteed in the closed cavity, and the closed cavity and the outside are guaranteed to be insulated.
(2) The bottom of the closed cavity is provided with a tray for placing the wooden floor sample, the wooden floor sample is heated through a heating cavity below the tray, the wooden floor sample and the heating cavity are in close contact, and heat in the heating cavity is conducted to the closed cavity in a radiation mode only through the wooden floor sample above the tray.
(3) The heating cavity is internally provided with a U-shaped water pipe, the water pipe is connected to the heating water tank on the outer side, and the water temperature in the heating cavity is controlled by the heating water tank, so that the temperature in the heating cavity can be constantly kept in a set temperature range.
(4) During the experiment, at first place wooden floor sample on the tray of closed intracavity through the epicoele, then close the closed chamber to take out the closed chamber with the vacuum pump and become required vacuum environment, detect the vacuum degree in the intracavity through the vacuum table on the closed chamber outer wall, begin to heat wooden floor sample on the tray through the heating chamber when reaching required experimental vacuum condition.
(5) A temperature sensor array is arranged in the closed cavity, the wood floor sample starts to release heat in a radiation mode into the closed cavity after being heated, temperature values of the sensors at different positions in the closed cavity at different moments are collected through the temperature sensor array, and the temperature values are stored in an upper computer.
(6) The temperature in the heating water tank is controlled through PID, the heating water tank and warm water in the heating cavity are kept in a flowing state, so that the temperature in the heating cavity is ensured to be constant, and when the temperature in the closed cavity reaches a balance state, the test is finished.
(7) And establishing a least square support vector machine prediction model and training by using the temperature value obtained on the space point corresponding to each sensor position. Training, namely machine learning training, namely training the least square support vector machine model through the obtained experimental data, wherein input variables are space coordinates and time of temperature values of sampling points, output variables are temperature values of the sampling points, the input variables and the output variables are respectively brought into the least square support vector machine model for machine learning, training parameters are adjusted to obtain a prediction model of the least square support vector machine with the best training effect, the model can be used for predicting the temperature values at any space position, and a curve of the temperature values along with time change is obtained.
(8) Because the number of the temperature sensors in the closed cavity is limited, more space point temperature values must be obtained to research the temperature field characteristics in the whole cavity, therefore, a plurality of prediction points need to be inserted between adjacent sensors in the closed cavity to form an incremental space point, and a trained least square support vector machine model is used for predicting the temperature values on the incremental space point.
(9) The predicted temperature values on the incremental space points and the temperature values acquired in the test are combined to form a new training sample, the least square support vector machine model is retrained by the sample to generate a new prediction model, and the temperature values on the next group of incremental space points are predicted, so that the temperature values on any space points can be obtained.
(10) When time factors need to be taken into consideration, temperature values of multiple times in the temperature balancing process in the closed cavity can be trained, and change curves of the temperature values at different space positions along with the time can be obtained.
Has the advantages that: by using the detection and research method aiming at the heat slow release rule of the ground heating wood floor, the heat release vacuum detection environment aiming at the heterogeneous material is established, the influence of two heat transfer modes of convection and heat conduction on the heat slow release characteristic of the ground heating wood floor can be removed, the research target is only aimed at one heat transfer mode of heat radiation, and the research object is more pertinent; meanwhile, a least square support vector machine is adopted to establish a temperature value prediction model for different space positions and different time in the closed cavity, the temperature value of a space point which cannot be directly measured and a curve of the temperature value changing along with the time can be predicted, the time and material cost of experimental research is reduced, the credibility of the research result of the heat release characteristic of the ground heating wood floor is increased, and scientific experimental and analytical tools can be provided for the subsequent heat release rule of the ground heating wood floor further dug deeply.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (1)
1. A detection and research method aiming at the heat slow release rule of a ground heating wooden floor is characterized in that,
(1) the thermal slow release detection device is arranged to be a cylindrical closed cavity, the upper surface and the lower surface of the closed cavity are of cambered surface structures, the upper surface is of an open-close type design, a wood floor sample to be tested is placed on a tray at the bottom of the closed cavity in a mode of opening the upper cavity, the cavity walls are double-layered, vacuum environments are arranged between the cavity walls and in the closed cavity, only one heat transfer mode of radiation is guaranteed to be in the closed cavity, and meanwhile, the closed cavity is guaranteed to be insulated from the outside;
(2) the bottom of the closed cavity is provided with a tray for placing the wood floor sample, and the wood floor sample is heated by a heating cavity below the tray and kept in close contact with the heating cavity, so that the heat in the heating cavity is only conducted to the closed cavity in a radiation mode through the wood floor sample on the tray;
(3) a U-shaped water pipe is arranged in the heating cavity, the water pipe is connected into a heating water tank on the outer side, and the water temperature in the heating cavity is controlled through the heating water tank, so that the temperature in the heating cavity can be constantly kept in a set temperature range;
(4) during the experiment, firstly, a wooden floor sample is placed on a tray in a closed cavity through an upper cavity, then the closed cavity is closed, a vacuum pump is used for pumping the closed cavity into a required vacuum environment, the vacuum degree in the cavity is detected through a vacuum meter on the outer wall of the closed cavity, and when the required test vacuum condition is achieved, the wooden floor sample on the tray is heated through a heating cavity;
(5) a temperature sensor array is arranged in the closed cavity, the wood floor sample starts to release heat to the closed cavity in a radiation mode after being heated, temperature values of sensors at different positions in the closed cavity at different moments are collected through the temperature sensor array and stored in an upper computer;
(6) the temperature in the heating water tank is controlled by PID, meanwhile, the heating water tank and warm water in the heating cavity are kept in a flowing state to ensure that the temperature in the heating cavity is constant, and when the temperature in the closed cavity reaches a balance state, the test is finished;
(7) establishing a least square support vector machine prediction model and training by using temperature values obtained on space points corresponding to positions of each sensor, wherein the training is machine learning training, the least square support vector machine model is trained through obtained experimental data, input variables are space coordinates and time of temperature values of sampling points, output variables are temperature values of the sampling points, the input variables and the output variables are respectively brought into the least square support vector machine model for machine learning, training parameters are adjusted to obtain the least square support vector machine prediction model with the best training effect, the model can be used for predicting the temperature values at any space positions, and a curve of the temperature values changing along with time is obtained;
(8) inserting a plurality of prediction points between adjacent sensors in a closed cavity to form an increment space point, and predicting a temperature value on the increment space point by using a trained least square support vector machine model;
(9) combining the predicted temperature values on the incremental space points with the temperature values acquired in the test to form a new training sample, retraining the least square support vector machine model by using the sample to generate a new prediction model, and predicting the temperature values on the next group of incremental space points to obtain the temperature values on any space points;
(10) the temperature values of a plurality of times in the temperature balancing process in the closed cavity are trained, and the change curves of the temperature values at different space positions along with the time can be obtained.
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